Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems

The Intelligent Transportation System (ITS) is an important part of modern transportation infrastructure, employing a combination of communication technology, information processing and control systems to manage transportation networks. This integration of various components such as roads, vehicles, and communication systems, is expected to improve efficiency and safety by providing better information, services, and coordination of transportation modes. In recent years, graph-based machine learning has become an increasingly important research focus in the field of ITS aiming at the development of complex, data-driven solutions to address various ITS-related challenges. This chapter presents background information on the key technical challenges for ITS design, along with a review of research methods ranging from classic statistical approaches to modern machine learning and deep learning-based approaches. Specifically, we provide an in-depth review of graph-based machine learning methods, including basic concepts of graphs, graph data representation, graph neural network architectures and their relation to ITS applications. Additionally, two case studies of graph-based ITS applications proposed in our recent work are presented in detail to demonstrate the potential of graph-based machine learning in the ITS domain

Lane-GNN: integrating GNN for predicting drivers’ lane change intention

Nowadays, intelligent highway traffic network is playing an important role in modern transportation infrastructures. A variable speed limit (VSL) system can be facilitated in the highway traffic network to provide useful and dynamic speed limit information for drivers to travel with enhanced safety. Such system is usually designed with a steady advisory speed in mind so that traffic can move smoothly when drivers follow the speed, rather than speeding up whenever there is a gap and slowing down at congestion. However, little attention has been given to the research of vehicles’ behaviours when drivers left the road network governed by a VSL system, which may largely involve unexpected acceleration, deceleration and frequent lane changes, resulting in chaos for the subsequent highway road users. In this paper, we focus on the detection of traffic flow anomaly due to drivers’ lane change intention on the highway traffic networks after a VSL system. More specifically, we apply graph modelling on the traffic flow data generated by a popular mobility simulator, SUMO, at road segment levels. We then evaluate the performance of lane changing detection using the proposed Lane-GNN scheme, an attention temporal graph convolutional neural network, and compare its performance with a temporal convolutional neural network (TCNN) as our baseline. Our experimental results show that the proposed Lane-GNN can detect drivers’ lane change intention within 90 seconds with an accuracy of 99.42% under certain assumptions. Finally, some interpretation methods are applied to the trained models with a view to further illustrate our findings

Combined Preconditioning and Postconditioning Provides Synergistic Protection against Liver Ischemic Reperfusion Injury

Hepatic Ischemia and Reperfusion Injury (IRI) is a major cause of liver damage during liver surgery and transplantation. Ischemic preconditioning and postconditioning are strategies that can reduce IRI. In this study, different combined types of pre- and postconditioning procedures were tested in a murine warm hepatic IRI model to evaluate their protective effects. Proanthocyanidins derived from grape seed was used before ischemia process as pharmacological preconditioning to combine with technical preconditioning and postconditioning. Three pathways related to IRI, including reactive oxygen species (ROS) generation, pro-inflammatory cytokines release and hypoxia responses were examined in hepatic IRI model. Individual and combined pre- and postconditioning protocols significantly reduce liver injury by decreasing the liver ROS and cytokine levels, as well as enhancing the hypoxia tolerance response. Our data also suggested that in addition to individual preconditioning or postconditioning, the combination of these two treatments could reduce liver ischemia/reperfusion injury more effectively by increasing the activity of ROS scavengers and antioxidants. The utilization of grape seed proanthocyanidins (GSP) could improve the oxidation resistance in combined pre- and postconditioning groups. The combined protocol also further increased the liver HIF-1 alpha protein level, but had no effect on pro-inflammatory cytokines release compared to solo treatment

Quantitative analysis reveals increased histone modifications and a broad nucleosome-free region bound by histone acetylases in highly expressed genes in human CD4+ T cells

AbstractGenome-wide mapping of nucleosomes and histone modifications revealed meaningful patterns. Despite advances in resolving the associations between chromatin and transcription, quantitative chromatin dynamics have not been well defined. We quantitatively determined differences in histone modifications, nucleosome positions, DNA methylation, and transcription factor binding in highly expressed and repressed genes in human CD4+ T cells. We showed that the first (−1) nucleosome upstream of the transcription start site (TSS) is shifted to the 5′ direction, thus forming a broad nucleosome-free region (NFR) near the TSS in highly expressed genes in CD4+ T cells. Moreover, the transcription factor YY1 and histone acetyltransferases bind the NFR with high affinity. Most of histone acetylations drastically increase in transcription activation (>5 folds). We also suggested that single nucleotide polymorphisms (SNPs) occur at a much lower frequency in highly expressed genes than in repressed genes. Our analysis quantitatively revealed details of chromatin dynamics

Nucleosome Positioning and Its Role in Gene Regulation in Yeast

Nucleosome, composed of a 147-bp segment of DNA helix wrapped around a histone protein octamer, serves as the basic unit of chromatin. Nucleosome positioning refers to the relative position of DNA double helix with respect to the histone octamer. The positioning has an important role in transcription, DNA replication and other DNA transactions since packing DNA into nucleosomes occludes the binding site of proteins. Moreover, the nucleosomes bear histone modifications thus having a profound effect in regulation. Nucleosome positioning and its roles are extensively studied in model organism yeast. In this chapter, nucleosome organization and its roles in gene regulation are reviewed. Typically, nucleosomes are depleted around transcription start sites (TSSs), resulting in a nucleosome-free region (NFR) that is flanked by two well-positioned H2A.Z-containing nucleosomes. The nucleosomes downstream of the TSS are equally spaced in a nucleosome array. DNA sequences, especially 10–11 bp periodicities of some specific dinucleotides, partly determine the nucleosome positioning. Nucleosome occupancy can be determined with high throughput sequencing techniques. Importantly, nucleosome positions are dynamic in different cell types and different environments. Histones depletions, histones mutations, heat shock and changes in carbon source will profoundly change nucleosome organization. In the yeast cells, upon mutating the histones, the nucleosomes change drastically at promoters and the highly expressed genes, such as ribosome genes, undergo more change. The changes of nucleosomes tightly associate the transcription initiation, elongation and termination. H2A.Z is contained in the +1 and −1 nucleosomes and thus in transcription. Chaperon Chz1 and elongation factor Spt16 function in H2A.Z deposition on chromatin. The chapter covers the basic concept of nucleosomes, nucleosome determinant, the techniques of mapping nucleosomes, nucleosome alteration upon stress and mutation, and Htz1 dynamics on chromatin

Origin and evolution of a placental-specific microRNA family in the human genome

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are a class of short regulatory RNAs encoded in the genome of DNA viruses, some single cell organisms, plants and animals. With the rapid development of technology, more and more miRNAs are being discovered. However, the origin and evolution of most miRNAs remain obscure. Here we report the origin and evolution dynamics of a human miRNA family.</p> <p>Results</p> <p>We have shown that all members of the miR-1302 family are derived from MER53 elements. Although the conservation scores of the MER53-derived pre-miRNA sequences are low, we have identified 36 potential paralogs of MER53-derived miR-1302 genes in the human genome and 58 potential orthologs of the human miR-1302 family in placental mammals. We suggest that in placental species, this miRNA family has evolved following the birth-and-death model of evolution. Three possible mechanisms that can mediate miRNA duplication in evolutionary history have been proposed: the transposition of the MER53 element, segmental duplications and Alu-mediated recombination. Finally, we have found that the target genes of miR-1302 are over-represented in transportation, localization, and system development processes and in the positive regulation of cellular processes. Many of them are predicted to function in binding and transcription regulation.</p> <p>Conclusions</p> <p>The members of miR-1302 family that are derived from MER53 elements are placental-specific miRNAs. They emerged at the early stage of the recent 180 million years since eutherian mammals diverged from marsupials. Under the birth-and-death model, the miR-1302 genes have experienced a complex expansion with some members evolving by segmental duplications and some by Alu-mediated recombination events.</p

A comparative study of using spatial-temporal graph convolutional networks for predicting availability in bike sharing schemes

Accurately forecasting transportation demand is crucial for efficient urban traffic guidance, control and management. One solution to enhance the level of prediction accuracy is to leverage graph convolutional networks (GCN), a neural network based modelling approach with the ability to process data contained in graph based structures. As a powerful extension of GCN, a spatial-temporal graph convolutional network (ST-GCN) aims to capture the relationship of data contained in the graphical nodes across both spatial and temporal dimensions, which presents a novel deep learning paradigm for the analysis of complex time-series data that also involves spatial information as present in transportation use cases. In this paper, we present an Attention-based ST-GCN (AST-GCN) for predicting the number of available bikes in bike-sharing systems in cities, where the attention-based mechanism is introduced to further improve the performance of an ST-GCN. Furthermore, we also discuss the impacts of different modelling methods of adjacency matrices on the proposed architecture. Our experimental results are presented using two real-world datasets, Dublinbikes and NYC-Citi Bike, to illustrate the efficacy of our proposed model which outperforms the majority of existing approaches